Computerized data processing and management has revolutionized industries and organizations of all types, including, but not limited to, government organizations and entities involved in education, banking/finance, health care, communications, and manufacturing. Further non-limiting examples include data management services, such as hosting services, library reference services (e.g. Lexis/Nexis or Westlaw) and the like.
As an organization grows, however, its reliance on computerized data processing and management will often increase. For example, a large manufacturing or service company, educational institution, or governmental organization may rely upon numerous databases and other data management systems. As but one specific example, an organization may maintain data management systems devoted to the organization's internal finances, additional systems dedicated to personnel/human resources purposes, and still further systems related to the substantive operations of the organization (e.g. inventory, materials, and sales data for a manufacturing enterprise).
In reality, the actual number and organization of systems may be even more complicated—for example, rather than a unified “human resources” data system, an organization may maintain employee performance data using a first data management system, insurance information in a second system, payroll information in a third system, and employee performance data in a fourth system.
The various data management systems may be associated with dedicated hardware in some instances. However, some data management systems may be supported by the same hardware. Nonetheless, data typically must be accessed separately according to the particulars of each system.
An issue that may even further complicate matters is incompatibility between various data systems. For example, various data processing systems may have been added over time as an organization expanded, and thus the systems may rely on different underlying software, syntax, and the like.
Yet another issue that arises in parallel with the number and arrangement of data access systems is the sheer volume of data. The actual amount of information available may be beyond the amount that any natural person could conceivably review manually, such as by viewing database records one-by-one without a specific search. Regardless of the amount of actual data, it may be maintained only in a computer-accessible form, precluding any sort of manual review.
From an end-user standpoint, the multiplicity of data sources and amount of available data in an organization can range from an inconvenience to a major problem. For instance, one or more people making a decision based on data housed in multiple sources will need access to the data in some sort of correlated form. The decisionmakers may desire specific information but may have little or no idea where or how the underlying data is organized. This may be especially frustrating for a decisionmaker who “knows” (through his or her experience and/or past activities with a particular issue) what the data “should” say, but needs hard data to back up his or her position.
The decisionmaker(s) may personally access and correlate the data, but this will generally take an appreciable amount of time and skill. In many instances, the decisionmaker(s) cannot efficiently access the data—for example, a middle manager may not be capable of constructing a set of SQL queries to access the desired information.
An alternative is to provide specialists to prepare data for the decisionmaker(s). However, the typical organization's resources (such as IT personnel) may be limited, and it may take time even for the specialists to access the desired data. Another consideration is that the data access specialists, while having a great deal of knowledge as to how to access the data, may not have the decisionmaker's depth of knowledge as to why the data is needed. Thus, even if detailed instructions are provided by the decisionmaker(s), the resulting data may not be exactly what the decisionmaker(s) need or had in mind, and the entire process may need to be repeated.